A novel immune-related prognostic signature in epithelial ovarian carcinoma

CXCL10 impairs synaptic plasticity and was modulated by cGAS-STING pathway after stroke in mice

Sensorimotor deficits following stroke remain a major cause of disability, but little is known about the specific pathological mechanisms. Exploring the pathological mechanisms and identifying potential therapeutic targets to promote functional rehabilitation after stroke are essential. CXCL10, also known as interferon-γ-inducible protein 10 (IP-10), plays an important role in multiple brain disorders by mediating synaptic plasticity, yet its role in stroke is still unclear. In this study, mice were subjected to photothrombotic (PT) stroke, and sensorimotor deficits were determined by the ladder walking tests, tape removal tests, and rotarod tests. The density of dendritic spines and synaptic plasticity was determined in Thy1-EGFP mice and evaluated by electrophysiology. We found that photothrombotic stroke induced sensorimotor deficits and upregulated the expression of CXCL10, whereas suppressing the expression of CXCL10 by adeno-associated virus (AAV) ameliorated sensorimotor deficits and increased the levels of synapse-related proteins, the density of dendritic spines, and synaptic strength. Furthermore, the cyclic GMP-AMP (cGAMP) synthase (cGAS)-stimulus of interferon genes (STING) pathway was activated by stroke and induced CXCL10 release, and cGAS or STING antagonists downregulated the levels of CXCL10 and improved synaptic plasticity after stroke. Collectively, our results indicate that cGAS-STING pathway activation promoted CXCL10 release and impaired synaptic plasticity during stroke recovery.NEW & NOTEWORTHY Chemokine-mediated inflammatory response plays a critical role in stroke. CXCL10 plays an important role in multiple brain disorders by mediating synaptic plasticity, yet its role in stroke recovery is still unclear. Herein, we identified a new mechanism that cyclic GMP-AMP (cGAMP) synthase (cGAS)-stimulus of interferon genes (STING) pathway activation promoted CXCL10 release and impaired synaptic plasticity during stroke recovery. Our findings highlight the potential therapeutic strategy of targeting the cGAS-STING pathway to treat stroke.

The Integrated Bioinformatic Approach Reveals the Prognostic Significance of LRP1 Expression in Ovarian Cancer

A hyperactive tumour microenvironment (TME) drives unrestricted cancer cell survival, drug resistance, and metastasis in ovarian carcinoma (OC). However, therapeutic targets within the TME for OC remain elusive, and efficient methods to quantify TME activity are still limited. Herein, we employed an integrated bioinformatics approach to determine which immune-related genes (IRGs) modulate the TME and further assess their potential theragnostic (therapeutic + diagnostic) significance in OC progression. Using a robust approach, we developed a predictive risk model to retrospectively examine the clinicopathological parameters of OC patients from The Cancer Genome Atlas (TCGA) database. The validity of the prognostic model was confirmed with data from the International Cancer Genome Consortium (ICGC) cohort. Our approach identified nine IRGs, AKT2, FGF7, FOS, IL27RA, LRP1, OBP2A, PAEP, PDGFRA, and PI3, that form a prognostic model in OC progression, distinguishing patients with significantly better clinical outcomes in the low-risk group. We validated this model as an independent prognostic indicator and demonstrated enhanced prognostic significance when used alongside clinical nomograms for accurate prediction. Elevated LRP1 expression, which indicates poor prognosis in bladder cancer (BLCA), OC, low-grade gliomas (LGG), and glioblastoma (GBM), was also associated with immune infiltration in several other cancers. Significant correlations with immune checkpoint genes (ICGs) highlight the potential importance of LRP1 as a biomarker and therapeutic target. Furthermore, gene set enrichment analysis highlighted LRP1's involvement in metabolism-related pathways, supporting its prognostic and therapeutic relevance also in BLCA, OC, low-grade gliomas (LGG), GBM, kidney cancer, OC, BLCA, kidney renal clear cell carcinoma (KIRC), stomach adenocarcinoma (STAD), and stomach and oesophageal carcinoma (STES). Our study has generated a novel signature of nine IRGs within the TME across cancers, that could serve as potential prognostic predictors and provide a valuable resource to improve the prognosis of OC.

Immunological modifications following chemotherapy are associated with delayed recurrence of ovarian cancer

Introduction

Ovarian cancer recurs in most High Grade Serous Ovarian Cancer (HGSOC) patients, including initial responders, after standard of care. To improve patient survival, we need to identify and understand the factors contributing to early or late recurrence and therapeutically target these mechanisms. We hypothesized that in HGSOC, the response to chemotherapy is associated with a specific gene expression signature determined by the tumor microenvironment. In this study, we sought to determine the differences in gene expression and the tumor immune microenvironment between patients who show early recurrence (within 6 months) compared to those who show late recurrence following chemotherapy.

Methods

Paired tumor samples were obtained before and after Carboplatin and Taxol chemotherapy from 24 patients with HGSOC. Bioinformatic transcriptomic analysis was performed on the tumor samples to determine the gene expression signature associated with differences in recurrence pattern. Gene Ontology and Pathway analysis was performed using AdvaitaBio's iPathwayGuide software. Tumor immune cell fractions were imputed using CIBERSORTx. Results were compared between late recurrence and early recurrence patients, and between paired pre-chemotherapy and post-chemotherapy samples.

Results

There was no statistically significant difference between early recurrence or late recurrence ovarian tumors pre-chemotherapy. However, chemotherapy induced significant immunological changes in tumors from late recurrence patients but had no impact on tumors from early recurrence patients. The key immunological change induced by chemotherapy in late recurrence patients was the reversal of pro-tumor immune signature.

Discussion

We report for the first time, the association between immunological modifications in response to chemotherapy and the time of recurrence. Our findings provide novel opportunities to ultimately improve ovarian cancer patient survival.

An artificial intelligence prediction model based on extracellular matrix proteins for the prognostic prediction and immunotherapeutic evaluation of ovarian serous adenocarcinoma

Background: Ovarian Serous Adenocarcinoma is a malignant tumor originating from epithelial cells and one of the most common causes of death from gynecological cancers. The objective of this study was to develop a prediction model based on extracellular matrix proteins, using artificial intelligence techniques. The model aimed to aid healthcare professionals to predict the overall survival of patients with ovarian cancer (OC) and determine the efficacy of immunotherapy. Methods: The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) data collection was used as the study dataset, whereas the TCGA-Pancancer dataset was used for validation. The prognostic importance of 1068 known extracellular matrix proteins for OC were determined by the Random Forest algorithm and the Lasso algorithm establishing the ECM risk score. Based on the gene expression data, the differences in mRNA abundance, tumour mutation burden (TMB) and tumour microenvironment (TME) between the high- and low-risk groups were assessed. Results: Combining multiple artificial intelligence algorithms we were able to identify 15 key extracellular matrix genes, namely, AMBN, CXCL11, PI3, CSPG5, TGFBI, TLL1, HMCN2, ESM1, IL12A, MMP17, CLEC5A, FREM2, ANGPTL4, PRSS1, FGF23, and confirm the validity of this ECM risk score for overall survival prediction. Several other parameters were identified as independent prognostic factors for OC by multivariate COX analysis. The analysis showed that thyroglobulin (TG) targeted immunotherapy was more effective in the high ECM risk score group, while the low ECM risk score group was more sensitive to the RYR2 gene-related immunotherapy. Additionally, the patients with low ECM risk scores had higher immune checkpoint gene expression and immunophenoscore levels and responded better to immunotherapy. Conclusion: The ECM risk score is an accurate tool to assess the patient's sensitivity to immunotherapy and forecast OC prognosis.

Construction and validation of an angiogenesis-related lncRNA prognostic model in lung adenocarcinoma

Background: There is increasing evidence that long non-coding RNAs (lncRNAs) can be used as potential prognostic factors for cancer. This study aimed to develop a prognostic model for lung adenocarcinoma (LUAD) using angiogenesis-related long non-coding RNAs (lncRNAs) as potential prognostic factors.

Methods: Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify aberrantly expressed angiogenesis-related lncRNAs in LUAD. A prognostic signature was constructed using differential expression analysis, overlap analysis, Pearson correlation analysis, and Cox regression analysis. The model’s validity was assessed using K-M and ROC curves, and independent external validation was performed in the GSE30219 dataset. Prognostic lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) networks were identified. Immune cell infiltration and mutational characteristics were also analyzed. The expression of four human angiogenesis-associated lncRNAs was quantified using quantitative real-time PCR (qRT-PCR) gene arrays.

Results: A total of 26 aberrantly expressed angiogenesis-related lncRNAs in LUAD were identified, and a Cox risk model based on LINC00857, RBPMS-AS1, SYNPR-AS1, and LINC00460 was constructed, which may be an independent prognostic predictor for LUAD. The low-risk group had a significant better prognosis and was associated with a higher abundance of resting immune cells and a lower expression of immune checkpoint molecules. Moreover, 105 ceRNA mechanisms were predicted based on the four prognostic lncRNAs. qRT-PCR results showed that LINC00857, SYNPR-AS1, and LINC00460 were significantly highly expressed in tumor tissues, while RBPMS-AS1 was highly expressed in paracancerous tissues.

Conclusion: The four angiogenesis-related lncRNAs identified in this study could serve as a promising prognostic biomarker for LUAD patients.

Molecular mechanism of Wilms' tumor (Wt1) (+/-KTS) variants promoting proliferation and migration of ovarian epithelial cells by bioinformatics analysis

Epithelial ovarian cancer (EOC) is a gynecological disease with the highest mortality. With the lack of understanding of its pathogenesis, no accurate early diagnosis and screening method has been established for EOC. Studies revealed the multi-faceted function of Wilms' tumor (Wt1) genes in cancer, which may be related to the existence of multiple alternative splices. Our results show that Wt1 (+KTS) or Wt1 (-KTS) overexpression can significantly promote the proliferation and migration of human ovarian epithelial cells HOSEpiC, and Wt1 (+KTS) effects were more evident. To explore the Wt1 (+/-KTS) variant mechanism in HOSEpiC proliferation and migration and ovarian cancer (OC) occurrence and development, this study explored the differential regulation of Wt1 (+/-KTS) in HOSEpiC proliferation and migration by transcriptome sequencing. OC-related hub genes were screened by bioinformatics analysis to further explore the differential molecular mechanism of Wt1 (+/-KTS) in the occurrence of OC. Finally, we found that the regulation of Wt1 (+/-KTS) variants on the proliferation and migration of HOSEpiC may act through different genes and signaling pathways and screened out key genes and differentially regulated genes that regulate the malignant transformation of ovarian epithelial cells. The implementation of this study will provide new clues for the early diagnosis and precise treatment of OC.

Protein expression profiling identifies a prognostic model for ovarian cancer

Background

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

Methods

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.

Results

394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.

Conclusions

The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.